A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Authors : Yılmaz KAYA

Pages : 2079-2091

Doi:10.3906/elk-1203-119

View : 16 | Download : 8

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set insert ignore into journalissuearticles values(RS); and extreme learning machine insert ignore into journalissuearticles values(ELM); methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset insert ignore into journalissuearticles values(WBCD);, derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.
Keywords : Breast cancer, rough set, extreme learning machine, expert system, artificial intelligence